Can you use autoregressive diffusion to generate market data?

Kavish's initial diffusion head, designed for market data generation, used a 1,000-step cosine schedule and DDIM for inference. This setup proved unstable, leading to exploding denoising trajectories where 88-95% of values exceeded 8 standard deviations from the mean across 7 continuous targets. The model's predictions also degraded significantly further into the future, as seen in the widening spread in CME US synthetic rollouts, indicating areas for further research.

This report details the instability of an initial diffusion model configuration, with 88-95% of values exceeding 8 standard deviations, unlike typical stable models.